Shocking language: Understanding the
macroeconomic effects of central bank communication∗
Stephen Hansen†
Michael McMahon‡
November 2, 2015
Abstract
We explore how the multi-dimensional aspects of information released by the FOMC
has effects on both market and real economic variables. Using tools from computational linguistics, we measure the information released by the FOMC on the state
of economic conditions, as well as the guidance the FOMC provides about future
monetary policy decisions. Employing these measures within a FAVAR framework,
we find that shocks to forward guidance are more important than the FOMC communication of current economic conditions in terms of their effects on market and
real variables. Nonetheless, neither communication has particularly strong effects
on real economic variables.
Keywords: Monetary policy; communication; Vector Autoregression.
JEL Codes: E52, E58
∗
We have benefited from comments during the ISOM conference in Zurich, and seminars at the
National University of Singapore and the IMF. We would like to especially thank Martin Bodenstein,
James Cloyne, Carlo Favero, Paul Hubert, Oscar Jorda, Christian Julliard, Dimitris Korobilis, Francesca
Monti, Helene Rey and Francisco Ruge-Murcia for insightful comments, suggestions and discussions. Paul
Soto and Marcel Schlepper provided excellent research assistance. Part of this work was completed when
both authors were Houblon-Norman Fellows at the Bank of England. Michael is currently based at the
IMF-STI. The views expressed in this paper are those of the authors and do not necessarily represent
those of anyone at the Bank of England, the IMF or IMF policy. We also benefited from a British
Academy small grant. Any errors remain ours alone.
†
Universitat Pompeu Fabra and GSE. Email: [email protected]
‡
IMF-STI, University of Warwick, CEPR, CAGE (Warwick), CfM (LSE), and CAMA (ANU). Email:
[email protected]
1
Introduction
It is now widely accepted that many aspects of modern monetary policy aim to manage
inflation expectations (King, Lu, and Pastén 2008). This is because economic agents
forward-looking decisions typically depend on expected real interest rates over reasonably
long horizons (up to, and beyond, 20 years for major investment decisions). Given that
the central bank controls nominal interest rates only at very short maturities, private
sector economic agents must take a view on both the likely future developments in the
economy, as well as the reaction of the central bank to these developments, in order to
establish their expectations of longer-term real interest rates.
Central bank communication has emerged as a key tool for central banks in their attempts to control inflation expectations. The Federal Open Market Committee (FOMC)
first accompanied their decision with a statement in February 1994 and although statements were ad-hoc for most of the 1990s, they are now a regular and closely-monitored
FOMC release. Blinder, Ehrmann, Fratzscher, Haan, and Jansen (2008), in their survey
of the large literature that has developed examining different aspects of communication
by monetary authorities, define central bank communication broadly as the information
that the central bank makes available about its current and future policy objectives, the
current economic outlook, and the likely path for future monetary policy decisions. An
important and open area in monetary policy is how to design central banks to optimise
their policy outcomes (Reis 2013), and the question of optimal communication strategy
is central to this discussion.
Before we can study optimal communication by central banks, we need to understand
the effects of different strategies on a variety of macroeconomic and market variables.
The novel empirical approach taken in this paper is to use techniques from computational
linguistics, applied to the statements of the FOMC, to measure the extent to which the
information provided is about the current outlook for the economy, and to what extent
it provides a guide for the future. This allows us to focus on multi-dimensional monetary
policy and we can contribute answers to two major questions in the literature. First,
we use our extracted measures of communication as variables in a Factor-Augmented
VAR (FAVAR) (Bernanke, Boivin, and Eliasz 2005) to examine the effect of central bank
communication on macroeconomic and financial variables. Second, we examine which
specific dimensions of monetary policy communication drive these effects.
To be more precise on the dimensions of monetary policy that we have in mind,
consider a central bank that, on average, makes decisions that are well-described by a
rule for nominal interest rates in the spirit of Taylor (1993):
it = f × Ωt + t
1
(1)
where f is the vector of reaction coefficients, Ωt is the vector of economic inputs to the
rule and t is the deviation from that rule at time t. Agents can use their knowledge of
this rule, together with expectations of the inputs to the decision, in order to form their
beliefs on future decisions and future interest rates.
When the central bank announces its decision at time t, it reveals it . It is the behaviour
of this interest rate variable that attracts most attention in the analysis of the effects of
monetary policy. We consider that the central bank can also communicate through its
statement, and we consider that this communication adds two additional dimensions to
monetary policy. Since we will empirically measure these two aspects that the central
bank can communicate about, we will be in a unique position to study the dynamic effects
of central bank communication. The two additional dimensions of monetary policy that
we consider are communication about:
State of Economy: the FOMC’s belief about the current and expected economic outlook Ωt .
Forward Guidance: the FOMC’s expected deviations from this average rule (t ), or a
commitment to follow some path that may deviate from the average rule.
Our main finding in this paper is that, at least in the US in the last 18 years, central
bank communication on future interest rates (forward guidance) seems to have been much
more important than their communication of current economic conditions. However, we
find that neither communication has particularly strong effects on real economic variables
in our FAVAR, especially relative to the effect of the actual policy stance.
Of course, issues of central bank communication have been studied before in both
theoretical models (for example, the model-based evaluation of central bank communication strategies in Eusepi and Preston (2010)), and there is also an emerging empirical
literature. For example, Ehrmann and Fratzscher (2007) examine the communication
strategies of the ECB, Bank of England and the Federal Reserve; Ranaldo and Rossi
(2010) examines the financial market effects of Swiss National Bank announcements;
Hayo and Neuenkirch (2010) considers the predictability of future Fed rates using information in announcements; Berger, Ehrmann, and Fratzscher (2011) looks at the ECB
and media reaction; and Hayo, Kutan, and Neuenkirch (2012) focuses on asset market
reactions to Fed communications.
A key motivating paper for this literature is Gürkaynak, Sack, and Swanson (2005)
(GSS). They show, using an event study approach analysing movements in financial
markets data around FOMC interest rate decisions, that central bank announcements
move markets.1 In fact, the statement accounts for most of the movements in 5- and
1
Specifically, they decompose the effects of FOMC announcements on financial markets into different
factors and reject that a single factor related to the policy actions sufficiently explains the movements.
Instead, they identify two factors in their analysis of FOMC statements from 1990 to 2004.
2
10-year Treasury yields. They conclude that expectations of future decisions are key and
that the statements are what help to affect investor expectations.2
While GSS is an important paper which indicates that central bank communication
reveals information to investors and thereby influences their expectations, a downside of
their methodology is that they do not measure the communication. Instead, the effects
of policy, and their identified ‘path factor’ is revealed from the immediate response of
particular asset prices. Though they find that “FOMC actions were priced into the federal funds futures market almost immediately”, the detail and complexity of the FOMC
statement has increased substantially since the financial crisis and especially since the deployment of unconventional monetary policy (Hernández-Murillo and Shell 2014).3 This
means that if the full understanding and reaction took longer (days), and the immediate response was only transitory, we might get a very misleading view of the effects
of the statements from this methodology. A second downside is that we do not learn
what information is being revealed to investors (Woodford 2012). Given that we measure
two specific aspects of the central bank communication directly, we can use these measures to assess the importance of each dimension. As such, we view our work as highly
complementary to the GSS event-study methodology.
The major empirical challenge for the analysis of central bank communication, and
one we address head on in this paper, is to convert the raw communication, which is
typically words, into meaningful quantities which we can systematically analyse. Some
approaches simply only focus on quantitative communication (such as released central
bank forecasts), while others use counts of some pre-selected keywords (as in Rosa and
Verga (2008)) to measure content. The main methodological contribution in this paper
is to use computational linguistics, and particularly the combination of topic modelling
and dictionary methods, in order to examine the content of what central banks are trying
to communicate to the markets and the public.
The first obvious advantage of the use of automated techniques rather than a purely
narrative approach to study the statements is scalability without concerns about consistency of the application of the method. With automated methods it is then easy to
extend the sample to include more recent data, other sources of communication such as
FOMC speeches, or to extend it to other central banks. The second advantage is precisely that the researcher does not have to worry that too much prior knowledge of the
big announcements is allowed to determine the choices made in creating the indices. Of
course, narrative methods might be able to pick up some of the nuance of statements
2
They write: “our results do not indicate that policy actions are secondary so much as that their
influence comes earlier when investors build in expectations of those actions in response to FOMC
statements (and perhaps other events, such as speeches and testimony by FOMC members).”
3
This is measured by both the length of the statement, which increased from 50-200 words in the
early 1990s, to more than 800 words in the first five meetings of Janet Yellen as Chair. This is reflected
in the estimated Flesch-Kincaid Grade Level increasing from a range of 9-14 to 18-19.
3
more precisely. We make use of both in this paper.
In terms of the computational approaches, we use Latent Dirichlet Allocation (LDA)
and dictionary methods to extract the content of official interest rate communications
(statements) by the Federal Reserve. LDA is widely used in linguistics, computer science, and other fields; the article that introduced it, Blei, Ng, and Jordan (2003), has
over 10,000 citations in 10 years. While computational linguistic models are used in
the political science literature, their use is still mainly descriptive; for example, Quinn,
Monroe, Colaresi, Crespin, and Radev (2010) use a topic model similar to LDA to study
congressional speeches to see what congress is talking about. We believe that the approach of using computational linguistics to create measures of communication from large
databases of text has broader applications beyond monetary policy analysis and can help
bringing economics into the increasingly important world of “Big Data”. Existing work
using computational linguistics tools to analyse monetary policy data include Bailey and
Schonhardt-Bailey (2008) and Schonhardt-Bailey (2013) who focus on arguments and persuasive strategies adopted by policymakers; Fligstein, Brundage, and Schultz (2014) who
apply LDA to the FOMC transcripts in order to examine the concept of “sense-making”
on the FOMC; Acosta (2015) looks at how the FOMC responded to calls for greater
transparency; and our own recent work examining the effect of transparency on the deliberation of the FOMC using LDA applied to FOMC transcripts (Hansen, McMahon,
and Prat 2014).
Hendry and Madeley (2010) and Hendry (2012) are closely related papers focusing on
Canada. The objective of both papers is to understand how central bank communication
affects markets, and both use text-mining tools in this endeavour. As well as different
tools from text-mining, and applying them to a different country, the main difference
between our paper and these papers is that we look at a broader set of reactions, whereas
these papers focus on the response of returns and volatility in interest rate markets.
The closest paper in the literature is Lucca and Trebbi (2009). They also applied computational linguistic tools to FOMC statements and measure the effects on the macroeconomy including in a VAR framework. The main contribution of our work relative to
their work is that we separately look at the effect of different dimensions of monetary
policy. We also apply different tools from computational linguitics (both LDA for topic
modelling and dictionary methods to measure tone). Finally, as a small difference, we examine the effects in a FAVAR which allows us to look at a wide variety of macroeconomic
effects, though our ordering variables is similar.
The remainder of the paper proceeds as follows. We first discuss the idea behind
the effects of central bank communication and how we measure these three dimensions
empiricially. We then introduce the macroeconometric methodology (FAVAR) before
exploring the results and concluding.
4
2
Dimension 1: Stance of current monetary policy
Before we turn to the measurement of communication, we begin by discussing the most
traditional dimension of monetary policy - the stance of current policy. Most studies
focus only on this single aspect of monetary policy. In the FAVAR model of Bernanke,
Boivin, and Eliasz (2005), as in VAR analyses in Christiano, Eichenbaum, and Evans
(1999) or Stock and Watson (2001), the effective Fed Funds rate (it ), is included as a
driving variable affecting the economy.
However, as our analysis covers 1998 to 2014, this period is significantly affected by the
zero lower bound (ZLB) on nominal interest rates. This is problematic because economic
conditions may be pretty poor, but since the FOMC cannot change the Federal Funds
Target Rate once it hits the ZLB, the estimated reaction to economic conditions would
be less than is otherwise the case. Moreover, there is a period around September 2008
during which the FFR was cut very aggressively as a result of the failure of Lehman
Brothers and the ensuing financial markets disruption, but a relatively large recession
followed nonetheless. Finally, given the FOMC made significant use of large-scale asset
purchases as a part of a credit-easing policy, the concern is that using it as the measure
of monetary stance is not at all appropriate.
The solution we adopt is to use the shadow rate data from Wu and Xia (2014).4
Using a shadow rate term structure model, the authors derive a measure st to assess the
current stance of monetary policy at the ZLB. This shadow rate is given by the minimum
value between the effective Fed Funds Rate and the shadow rate. This means that the
monetary stance is measured by the effective Fed funds rate when interest rates are above
the ZLB, but can become negative at the ZLB. Figure 1 plots the measure of monetary
stance (st ) that we use.
3
Dimension 2: Views about the economy
Given the lags in the availability of economic data, and the fact that monetary policy
decisions are made as forward looking decisions, the FOMC make decisions using an
information set that may differ from those of the public. As such, the second dimension
of monetary policy that the FOMC can provide information on is its beliefs about the
state of the economy.
We derive empirical measures using a novel approach to combine “the two Ts”: Topic
and Tone. That is, we need to know first whether the central bank is talking about the
state of the economy, Ωt , the topic, and then we need to measure how they are talking
about it (tone). In this paper, we make use of Latent Dirichlet Allocation (LDA) to
4
There are other similar approaches to calculating a shadow rate including Bauer and Rudebusch
(2013) and Krippner (2013).
5
8 6 4 2 0 Wu Xia Monetary Stance (s) -­‐2 -­‐4 199801 200001 200201 200401 200601 200801 201001 201201 201401 Figure 1: Federal Reserve Monetary Stance: Shadow rate (Wu and Xia 2014)
measure when they are talking about economic topic and a balance measure based on
dictionary methods, or word counting, to measure tone. Our proposed way of combining
these two approaches allows us to measure topic-level tone which helps to deal, somewhat, with the weakness of dictionary methods. That is, rather than just measure words
associated with expansion, we can measure expansion words associated with GDP growth
rather than risk premia. We now discuss in more detail our empirical strategy to measure
the FOMC statements on the state of the economy.
3.1
Measuring Economic Topics using LDA
LDA is a very popular algorithm developed by Blei, Ng, and Jordan (2003) and used for
information retrival. Here we use it to discover the topic of each sentence of the FOMC
statements. In this subsection we outline the basic steps and intuition for the algorithm.
Hansen, McMahon, and Prat (2014) provide a full description along with the statistical
foundations.5
LDA is essentially a very fleixble clustering algorithm for words that groups words
into topics on the basis of repeated co-occurrence across paragraphs. There are two
inputs to the algorithm. The first input that the user must supply is a corpus of the
documents of text to be analysed; in this paper the corpus is the full history of FOMC
statements accompanying decisions on monetary policy where we group words at the
level of an individual paragraph in a statement. However, before using the words in the
LDA analysis, we first remove stop words (such as ‘the’, ‘a’ and ‘and’) and also stem
5
Blei and Lafferty (2009) contains an overview of LDA and some of its extensions.
6
the remaining words which reduces them to a common linguistic root (‘economy’ and
‘economic’ both become ‘economi’). The second input is a number of topics that the
algorithm should form; we use a 15-topic model.
The are two broadly defined outputs. The algorithm will form, in our case, 15 topics
which are probability distributions over words and tell the user the words which tend to
go together. The algorithm also forms document distributions which contain probabilities
that capture the fraction of words policy makers devote to the different topics in their
communications. For example, it might suggest that a sentence in a statement (our level
of LDA analysis) is 0.75 about topic A and 0.2 about topic B and so on.6
To get more precise, topic models estimate K topics each of which is a distribution
βk ∈ ∆V over the V unique tokens (words) in the corpus vocabulary. LDA is flexible
enough to allow unique tokens to belong to more than one topic. LDA will also generate
bd ∈ ∆K for each document, where ∆K is the Ka predictive distribution over topics θ
simplex. However, given that we estimate the topic model at the sentence level, rather
than use the predictive distribution, we prefer to work with the word to topic allocations
bd ). In particular,
directly (this is an intermediate step in the LDA algorithm to generate θ
let φp,k,d = np,d (k)/np,d be the fraction of sentence p words allocated to topic k, where
np,d (k) is the number of sentence p words allocated to topic k, and np,d is the total number
of words in the paragraph. We will define a sentence as being about topic k when this
estimated topic allocation fraction φp,k,d is greater than some critical proportion (α).
In fact, we estimate the LDA model using a collapsed Gibbs sampling algorithm. As
such, we get measures of topic allocation for every iteration of the chain. The data that
we work with has been extracted from the best-performing (in an information matching
sense) chain but we draw 20 samples from points in the chain that are thinned using a
thinning interval of 50. We then take an average over the 20 samples.
We estimate our 15-topic LDA on the full corpus of 142 FOMC decision statements,
split into sentences, up to March 2015 (although we will estimate our FAVAR on a
slightly shorter sample of the data between 1998 and 2014). The LDA-estimated topics
cover different aspects of the FOMC communication. We select five topics which relate to
the discussion of the economic situation. The key words (tokens) in the economic topics
are presented as word clouds in figure 2:7
Topic 2: A topic which focuses on inflation and prices.
Topic 14: Another topic concerning inflation and prices.
Topic 4: A topic covering the demand side of the economic outlook.
6
Once estimated at a given level of aggregation, it is possible to aggregate document distributions up
using a process called querying. See Hansen, McMahon, and Prat (2014) for details.
7
Note that the figure plots the stemmed tokens as these are the unit of LDA analysis.
7
Topic 6: A topic about the labour market issues.
Topic 9: A topic covering the prospects for growth.
3.2
Measuring tone with dictionary methods
Once we identify those sentences that are about the economic situation topics, we using
only these relevant sentences to create our time-series balance measure of the FOMC
statement on the economic situation using dictionary methods, or more simply, word
counting. This is a common way of measuring market sentiment in the finance literature,
where word lists are chosen to reflect positive and negative tone and applied to media text
or company results releases; see, for example, Tetlock (2007), Tetlock, Saar-Tsechansky,
and Macskassy (2008), Loughran and McDonald (2011) and Loughran and McDonald
(2014).
The idea is as follows. Let ` = (t1 , . . . , tN ) be a list of unique terms and d be a
document, which we can also think of as a list of (possibly non-unique) terms. We can
then define nd (`) to be the raw count of terms in ` in document d, and either use this
alone to index d, or else apply some normalization (like dividing by the total number
of terms in d). Our approach to combining the tone and topic algorithms is to view a
document as an ordered sequence of sentences d = (π1,d , . . . , πΠd ,d ) where Πd is the total
number of sentences in document d. We identify the sentences in which topic k makes up
at least α fraction of attention as measured by φp,k,d allocation variable defined earlier.
Then, within this set of sentences, compute the fraction of words that lies in list ` and
normalise by the total number of words in sentences.
To measure the tone of the sentences on the economic situation, we use “directional”
word lists measuring words associated with expansion and contraction as used in Apel
and Blix Grimaldi (2012). For example, in table 1 we list some of the words that we
associate with contraction and expansion.8 Of course, these methods work best at finer
and finer levels of topic disagregation. Increasing risk is not typically a sign of economic
expansion but by isolating topics related to the economy, we hopefully have (at least
partly) corrected for this.
Using those sentences about the economic situation, we create our time-series balance
measure of the FOMC statement on the economic situation as follows:
EcSitt =
nP os,t − nN eg,d
T otalW ordsEC
t
8
(2)
The appendix contains the full list of words that we use in the analysis in this paper along with their
frequency of occurence. This list does not include words which we looked for but which were not found
in the FOMC statements.
8
(a)
Topic 2
(b)
Topic 14
(c)
Topic 4
(d)
Topic 6
(e)
Topic 9
Figure 2: Topics Covering FOMC views of the Economic Situation
9
Table 1: Example of Contraction and Expansion Words
Contraction
decreas*
decelerat*
slow*
weak*
low*
loss*
contract*
Expansion
increas*
accelerat*
fast*
strong*
high*
gain*
expand*
Notes: * indicates that any word ending is acceptable.
where nP os,t (nN eg,t ) is the number of posive (negative) words in those sentences about the
economy, and T otalW ordsEC
is the total number of words about the economic situation.9
t
This gives a balance measure which can be greater than zero (more words associated with
expansion) or less than zero (more contraction words).
For example, consider the following line on the economy from the January 2010 Statement:
“Household spending is expanding at a moderate rate but remains constrained by a weak labor market, modest income growth, lower housing wealth,
and tight credit.”
This sentence is about topic 4 and it contains 18 words, of which one is expansionary
(expanding) and three are contraction words (lower, moderate, weak). On its own, it
. But, in fact, we aggregate all the lines about the
would get a balance score of −2
18
economy from that statement and create the balance on the aggregated text which in
this case yields an overall negative balance (-0.07). We repeat this excercise is completed
for every statement, conducting the analysis on statements about one of the economic
topics.
Figure 3 shows the constructed index as bars (with each bar representing an FOMC
statement after a meeting). As can be seen there are breaks in the monthly time-series
of these contructed indices that affect the use of the series as a monthly time-series. This
is because in some months there is no FOMC meeting and as such there is no time-series
for that month. In these cases, we simply use value of the statement in the last meeting.
If there was a statement but no mention of the economic situation, as occurred in the
mid-1990s, the value of the index would be zero.
9
Of course, it is possible to think at an even greater level of disaggregation, such as trying to measure
the extent to which the statement reveals new information about the labour market, or price developments, but we leave that for future research.
10
0.1 0.05 0 -­‐0.05 -­‐0.1 -­‐0.15 Statement Economic Situa9on Balance Economic Situa9on Balance Used -­‐0.2 20
02
20 01 02
20 07 03
20 01 03
20 07 04
20 01 04
20 07 05
20 01 05
20 07 06
20 01 06
20 07 07
20 01 07
20 07 08
20 01 08
20 07 09
20 01 09
20 07 10
20 01 10
20 07 11
20 01 11
20 07 12
20 01 12
20 07 13
20 01 13
20 07 14
20 01 14
07
-­‐0.25 Figure 3: EcSitt : Statement by statment and monthly index
4
Dimension 3: Forward Guidance
The basic idea of forward guidance that we wish to capture is communication after
meeting t that captures the forward looking views of the committee as to how they
see interest rate decisions in future meetings. One issue is the extent to which any
forward guidance is Delphic or Odyssean as described by Campbell, Evans, Fisher, and
Justiniano (2012). The distinction, related to the Greek classical stories, is whether the
FOMC provides information about their view of the future (‘Delphic’) or whether they
commit themselves to a future path of interest rates (‘Odyssean’). Such a distinction,
and how one interpets FOMC forward guidance, is not uncontroversial as the Brookings
meeting discussion of the Campbell, Evans, Fisher, and Justiniano (2012) paper makes
clear. In this paper, we will not get a distinction that is perfectly Delphic or Odyssean.
Rather we shall measure the direction of guidance, the amount of guidance given and the
certainty in their statements about expected future path of interest rates. We shall not
distinguish between whether this is because they are committing to a particular path in
the Odyssean sense, they are signalling a forecast of the future direction of changes in
the economic outlook (Meyer 2012), or whether they think that other objectives, beyond
their usual ones, are driving likely decisions more (Romer 2012).
4.1
Manually Identifying Statements about Forward Guidance
In order to identify the relevant paragraphs in each statement, we use the narrative
approach. Specifically we employ a research assistant, guided by the list in Campbell,
11
Evans, Fisher, and Justiniano (2012), to select the statements related to discussions of
future decisions. The forward guidance paragraphs capture conditional statements about
the extent of monetary support going forward, the date-based guidance of the FOMC in
recent recent years, and also FOMC statements about the balance of risks as seen by the
FOMC.
As an example of the first kind, we capture statements such as from December 2013:
“To support continued progress toward maximum employment and price stability, the
Committee today reaffirmed its view that a highly accommodative stance of monetary
policy will remain appropriate for a considerable time after the asset purchase program
ends and the economic recovery strengthens.”
For the second type, we capture statements such as that of June 2012: “To support
a stronger economic recovery and to help ensure that inflation, over time, is at the rate
most consistent with its dual mandate, the Committee expects to maintain a highly
accommodative stance for monetary policy. In particular, the Committee decided today
to keep the target range for the federal funds rate at 0 to 1/4 percent and currently
anticipates that economic conditions–including low rates of resource utilization and a
subdued outlook for inflation over the medium run–are likely to warrant exceptionally
low levels for the federal funds rate at least through late 2014.”
For the last type, the August 1999 statement contains an example: “Today’s increase
in the federal funds rate, together with the policy action in June and the firming of
conditions more generally in U.S. financial markets over recent months, should markedly
diminish the risk of rising inflation going forward. As a consequence, the directive the
Federal Open Market Committee adopted is symmetrical with regard to the outlook for
policy over the near term.”
In this sense we are slightly broader than the typical research design that assumes
that August 2003 was the first use of forward guidance. In particular, that statement
pointed out:
“The Committee perceives that the upside and downside risks to the attainment of sustainable growth for the next few quarters are roughly equal.
In contrast, the probability, though minor, of an unwelcome fall in inflation
exceeds that of a rise in inflation from its already low level. The Committee judges that, on balance, the risk of inflation becoming undesirably low
is likely to be the predominant concern for the foreseeable future. In these
circumstances, the Committee believes that policy accommodation can be
maintained for a considerable period.”
12
4.2
Measuring Amount, Direction and Certainty of Guidance
In deciding how to measure the extent of forward guidance, one clear thing is that if there
are no words about future interest rates, there is no forward guidance. The other thing
that should be clear is that guidance can, as it typically is, suggest more expansionary
policy or, much more rarely, likely contractionary policy.10 Finally, there are occasions
when the guidance is more clear cut, and others when the FOMC is more cautious in its
guidance.
Once the forward guidance paragraphs have been identified manually, it is trivial to
determine the direction of guidance. In particular, as we plot for each of the statement
dates in Figure 4a, we classify a statement about more expansionary monetary policy as
−1, a neutral stratement as 0 and a statement about contractionary monetary policy as
+1.
To measure the amount of guidance given we could choose between measuring the
number of words dedicated to the paragraphs about forward guidance, or we could normalise this measure relative to the whole statement (measuring the share of the statement
dedicated to forward guidance. Given the trend increase in the length of statements, we
choose to measure the amount of guidance using the latter share measure.11 This is plotted in Figure 4b and shows the committee provided most quantity of guidance around
2009 and then from the middle of 2012.
Finally, in order to measure how ‘certain’, as opposed to cautious, the FOMC is in
their statement about forward guidance, we return to using dictionary methods described
above. For this we use the ‘ambiguity’ word list developed by Loughran and McDonald
(2011) and augment it with some words used specifically to convey certainty or uncertainty in monetary policy. To measure this aspect of the paragraph, we use:
Uncertaintyt =
nU ncertainty,t
nFt G
(3)
where nU ncertainty,t is the number of uncertain words used in the paragraphs about forward
guidance at time t, and nFt G is the total number of words about forward guidance at time
t.12
10
The May 2006 statement is an example: “The Committee judges that some further policy firming
may yet be needed to address inflation risks but emphasizes that the extent and timing of any such firming
will depend importantly on the evolution of the economic outlook as implied by incoming information.
In any event, the Committee will respond to changes in economic prospects as needed to support the
attainment of its objectives.”
11
However, the overall indexed (once normalised) is almost identical whichever of the measures we
choose. This is shown in Figure 5.
12
As an alternative approach, we could use the certainty/uncertainty measure as a signal for the
variance of future monetary policy shocks. We leave this for future research.
13
1.5 1 Direc1on 0.5 0 -­‐0.5 -­‐1 -­‐1.5 199809 200101 200301 (a)
200502 200701 200901 201101 201301 Direction of Guidance
60 Share of FG (%) 50 40 30 20 10 0 199809 200101 (b)
200301 200502 200701 200901 201101 201301 Share of statement dedicated to Guidance
0.1 0.09 Uncertainty Balance 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 199809 (c)
200101 200301 200502 200701 200901 201101 201301 Uncertainty words in Guidance Statement
Figure 4: Components of FG index
14
4.3
The Overall F Gt Index
Our overall index of forward guidance is then a combination of the three forward guidance
measures as follows:
FGt =
ShareFGt × DirectionFGt
.
Uncertaintyt
(4)
We normalise this measure such the largest negative value (the instance of the largest,
relatively certain, expansionary forward guidance statement) is given by -1. Figure 5
shows the constructed index both as bars (representing an FOMC statement) and as
the monthly series in which we fill in the gaps according the last statement. This index
picks up nicely that, since late 2008, the FOMC have used their strongest ever forward
guidance suggesting expansionary monetary policy. The index actually hits its lowest
point at the end of 2012 when the Fed retain the ‘considerable time’ phrase in their
expectations about continued easing monetary policy, but they also add more discussion
about the bond-buying program and the fact that interest rates will remain near zero for
a considerable amount of time after the conclusion of the bond-buying programme.
0.2 0 -­‐0.2 Statement Forward Guidance Index -­‐0.4 Forward Guidance Index Used (FG) Forward Guidance Index Words (FGwords) -­‐0.6 -­‐0.8 -­‐1 20
02
20 01 02
20 07 03
20 01 03
20 07 04
20 01 04
20 07 05
20 01 05
20 07 06
20 01 06
20 07 07
20 01 07
20 07 08
20 01 08
20 07 09
20 01 09
20 07 10
20 01 10
20 07 11
20 01 11
20 07 12
20 01 12
20 07 13
20 01 13
20 07 14
20 01 14
07
-­‐1.2 Figure 5: FGt : Statement by statment and monthly index
5
Econometric Methodology: FAVAR Analysis
We use a Factor-Augmented Vector Autoregression model (FAVAR), as developed by
Bernanke, Boivin, and Eliasz (2005), in order to investigate the effects of the extra
15
dimensions of the monetary policy announcements that we measure using the two timeseries indices. The FAVAR model considers:
Driving Variables Yt : M observed variables (each from t = 0, 1, ..., T ) which are assumed to drive the economy.
Unobserved factors Ft : K factors which capture the evolution of unobserved state
variables which drive the economy.
Observed economic time series Xt : N time-series which we are interested in understanding the evolution of in reaction to shocks.
The structure of the relationships between these variables is given by:
"
Ft
Yt
#
"
= Φ(L)
Ft−1
Yt−1
#
+ vt
(5)
where
Xt = ΛF Ft + ΛY Yt + et
(6)
where equation (6) is called the ‘observation equation’ and it tells us that Ft and Yt
are the driving forces of the observed economic time series, and equation (5) is called
the ‘transition equation’. 13 This framework would be a standard VAR if we omit Ft
and instead include important time-series in Yt . However, if we have omitted important
information then our VAR estimates are biased and can lead to very misleading results.
The classic price puzzle is an example of this. The FAVAR approach allows us to include
(and look at the reaction of) a large number of variables without running into the curse
of dimensionality.
In the original baseline FAVAR model of Bernanke, Boivin, and Eliasz (2005), only the
Fed Funds Target rate is included as a driving variable affecting the economy (Yt = [it ]).
Moreover, there is a single factor (K = 1).14
We have three dimensions of the monetary policy announcements - the description
of the economic situation (EcSitt ), the current stance (st ) and the forward guidance
(F Gt ). We will estimate our multi-dimensional monetary policy FAVAR using four factors
13
Here it is written as order 1 (1 lag) but any order p version can be written as a VAR(1) using the
‘companion form’.
14
One issue with the standard FAVAR approach is that it is not possible to impose that some factors
can react to the policy shocks because the factors have no labels. Belviso and Milani (2006) estimate a
‘structural FAVAR ’ in which they actually identify specific titles for the factors.
16
(K = 4) and the three measures included in the Yt vector:


EcSitt


Yt =  st  .
FGt
5.1
(7)
Steps in the estimation of the FAVAR model
We estimate the FAVAR defined by equations (5) and (6) using the two-step approach
that uses principle components to estimate the factors:
1. estimate the factors using principal components - F̂t .
2. estimate the VAR in F̂t and Yt .
As there are identification assumptions made in both steps, we shall now be more precise
on these two steps. As our approach follows closely the approach of Bernanke, Boivin,
and Eliasz (2005), readers familiar with FAVAR analysis can skip to section 5.2 which
outlines the identification approach specific to this paper.
5.1.1
Step 1: Estimation of F̂t
We extract the first K + M (number of factors plus number of Yt variables) principal
components of Xt which is called Ĉ(Ft , Yt ). These are linear combinations of Ft and Yt .
We are interested in identifying the structural shocks to all (or at least a subset) of
the Yt variables but we cannot identify the shocks if the estimated factors include the
effects of Yt . Essentially, the problem is that the approach to estimating the principal
components does not account for the fact that Yt is observed. Therefore we need to purge
the Ĉ(Ft , Yt ) of the effects of the Yt variables that we are interested in shocking.
We follow the identification approach of Bernanke, Boivin, and Eliasz (2005) that has
also been used many by others since:
Identification Assumption 1 A subset of Xt do not react contemporaneously to shocks
to Yt ; we call these ‘slow-moving variables’. We can therefore use the principal components across these variables to identify the F̂t to use in the FAVAR.
Precisely, we:
1. estimate the principal components in the slow-moving Xt variables and call these
Ĉ ∗ (Ft ); under the identification assumption 1 these principal components do not
contain reaction to Yt .
2. regress
Ĉ(Ft , Yt ) = βc Ĉ ∗ (Ft ) + βy Yt + ηt
17
(8)
3. Define:
F̂t = Ĉ(Ft , Yt ) − βy Yt
5.1.2
(9)
Step 2: Estimation of a VAR in F̂t and Yt
We then estimate a standard VAR using Bayesian estimation. Define:
"
Zt =
F̂t
Yt
#
(10)
Then (5) becomes our reduced form (estimated) model:
Zt = AZt−1 + vt
(11)
0
with vt the reduced-form residuals satisfying E vt vt = Ω. This estimation gives us Â
and Ω̂.
If we consider that there is a true structural model of the economy in which:
HZt = BZt−1 + ut
(12)
where ut are the structural shocks we are interested in and the structural variance 0
covariance matrix is given by E ut ut = D.
We can map the reduced form estimates to the strutural model using:
Zt = H −1 BZt−1 + H −1 ut
(13)
and noting that Â = H −1 B, v̂ = H −1 ut and, the key for identification
as it is ithe
h
0
0
only equation linking observables and structural coefficients, Ω̂ = E H −1 ut ut H −1 =
0
H −1 DH −1 . To map the estimated variance-covariance matrix of residuals to H −1 we
2
need restrictions on the coefficients in D and H −1 ; Ω̂ only provides N 2+N unique values
(since symmetric).
Identification Assumption 2 Through restrictions on the coefficients of structural variancecovariance matrix (D = IN ), as well as assuming that H −1 is lower triangular (Choleski
indentification), we can identify the H −1 matrix from the Ω̂ estimates.
The first part of identification assumption 2, assuming the structural shocks are independent from one another and also normalisation of the variance of the structural shocks
2
to 1, provides all but N 2−N restrictions on H −1 . Assuming that H −1 is lower triangular,
2
then we get N 2−N zero restrictions. This Choleski identification amounts to ordering restrictions: a lower triangular H −1 says that the reduced form residual for the first ordered
18
variable depends only on its own structural shock, the second variable depends on its own
shock and the shock to the first variable, and so on for each variable.
5.2
Using the framework to measure the impact of statements
We estimate our FAVAR using monthly data. The sample period used is January 1998 to
December 2014. We start in 1998 in order to concentrate on a period in which the FOMC
was making statements after all their meetings. This is also the start of the period during
which the FOMC was more likely to both describe the economic situation as well give
some guidance on the expected future path of interest rates. We end in December 2014.
This means that the total time series dimension is 204 monthly observations.
In this paper, as described in equation (7) above, we include our three policy variables
in Yt of our FAVAR. The Choleski ordering identification means that Federal Funds Rate
decisions at time t depend on lagged values of all the endogenous variables, as well as
shocks to the economic factors and the FOMC view of the economic situation as measured
by our balance index. Shocks to forward guidance are, by identification assumption 2,
assumed not to affect the current interest rate decision.
We include four factors estimated using principle components on the Xt time-series
data. Our Xt matrix of time-series variables contains 76 variables. Appendix A presents
the list of time-series data used, the sources as well as how we transform the data. As
required by identification 1, we need to define which variables react contemporaneously
with policy changes and which are ‘slow-moving’. The appendix provides the full list,
but broadly we consider markets data to be fast-moving and most macro variables to be
slow-moving.
We estimate the FAVAR using Gibbs Sampling with 20,000 draws sampled after a
burn-in of 10,000 draws and then we thin the 20,000 draws down to 400 draws by keeping
only every 50th sample along the chain. The confidence bands provided with estimates
are derived using the estimated distribution of 4000 draws. In the analysis, we use 7 lags.
6
Results
First we examine the effect of shocks to the FOMC’s monetary stance using analysis
of impulse response functions (IRF). Unlike traditional monetary policy shocks papers,
we then shift our attention to the statement effects in terms of forward guidance (F Gt )
and shocks to the assessment of the economic situation (EcSitt ). After the impulse
response analysis, we examine the contribution of these shocks to the variance of US
macroeconomic data. The results presented here are for the FAVAR estimated with 7
lags (monthly data) and with three factors included, using the sample from January 1998
19
to December 2014. The results are similar if we use two or four factors, and also if we
use 4 lags or 13 lags.
6.1
The effect of a change in FOMC monetary stance
We here examine the effects of traditional monetary policy shocks, namely those arising
from shocks to the Federal Funds Rate (FFR) and, at the ZLB, asset purchase shocks.
Figures 6 to 9 present the impulse responses to such a shock. Although this is the
standard type of monetary policy shock, it is worth noting that our inclusion of two
additional policy variables may capture some of the effects that would typically be part
of the monetary policy shock. For example, if on a given date the Fed has a more positive
view of the economy than the (lagged) data suggests, this might be typically captured
as a deviation from the normal monetary policy rule (a monetary shock) whereas in our
framework this is captured by the EcSitt index.
Impulse response of Econ Sit (EcSit)
2
1
0
-1
-2
3
6
9
12
15
18
21
24
27
30
33
36
30
33
36
30
33
36
Impulse response of Monetary Stance (s)
1
0
-1
-2
3
6
9
12
15
18
21
24
27
Impulse response of Fwd Guidance (FG)
1
0
-1
-2
3
6
9
12
15
18
21
24
27
Figure 6: IRF Response to Monetary Stance (st ) shock: Policy Variable Reaction
Figure 6 presents the shock that we analyse. Perhaps due to the period that we
estimate (1998-2014), the shock is quite persistent. This is partly as a result of being
directly persistent, but also because expansionary policy is found to typically lead to
expansionary forward guidance which itself pushes down on the monetary stance.
The result is that market yields in a number of fixed income markets are pushed down
persistently and across the yield curve (Figure 7). shows that the effect of this shock on
market rates is to raise rates across the yield curve. The effect is greatest at the shorter
end of the yield curve such that the yield curve twists down. Corporate yields also fall.
20
2
Impulse response of
3mYield
0
2
0
-2
Impulse response of
5yrYield
2
-2
3 6 9 121518212427303336
1
Impulse response of
3yrYield
0
-2
3 6 9 121518212427303336
2
Impulse response of
1yrYield
Impulse response of
10yrYield
3 6 9 121518212427303336
2
Impulse response of
Spr10y-2y
0
0
0
-1
-2
1
-2
-2
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
aaaYield
Impulse response of
baaYield
Impulse response of
SprAAA-10y
1
2
0
0
0
-1
-1
-2
3 6 9 121518212427303336
-2
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 7: IRF Response to a Monetary Stance (st ) shock: Yields Reaction
The reaction of many of the market variables is imprecisely estimated (figure 8). A
decrease in the monetary stance tends, with a lag, to increase confidence, and reduce
measures of uncertainty and volatility. It also pushes up on equity prices but this effect
is very imprecisely estimated.
The effect on real variables is also somewhat imprecisely estimated. Figure 9 shows
the responses. Nonetheless, the effect of a monetary easing is to lower unemployment
and prices (e.g. CPI) and to push up on measures of economic activity. These effects
tend to take around 18 months to take effect.
6.2
The effect of a change in Forward Guidance
We next look at the response of a change to the forward guidance element of the FOMC
statement FGt . The shock, shown in 10, involves the FOMC communicating an expansionary stance about the future decisions on interest rates; a negative shock is, in our
interpretation, more forward guidance.
The shock has the desired effect on market rates as shown in figure 10. As might
be expeected given the typical deployment of forward guidance at a time when shortterm rates are historically low, there is little near-term effect on shorter maturity bonds.
However, more expansionary forward guidance about future rates tends to decrease longer
maturity bonds significantly. It also plays a role in driving corporate bond yields including
in the near term after the statement.
These results seem longer lived than the findings of Wright (2012). He uses a daily
21
2
Impulse response of
Michigan
2
Impulse response of
BBD
2
1
1
1
0
0
0
-1
200
-1
Impulse response of
Vix
-1
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
SP500
Impulse response of
NASDAQ
Impulse response of
EquityIndexWilson
400
100
100
50
200
0
0
0
-100
3 6 9 121518212427303336
Impulse response of
×10 7USDTWImajor
20
-50
3 6 9 121518212427303336
0.5
Impulse response of
GasPrice
3 6 9 121518212427303336
1
Impulse response of
OilPrice
0
10
0
-0.5
0
-1
3 6 9 121518212427303336
-1
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 8: IRF Response to Monetary Stance (st ) shock: Markets Reaction
4
Impulse response of
IP
×10 6
2
Impulse response of
ISMManProd
2
1
1
0
0
Impulse response of
ISMNMActivity
2
0
-1
3 6 9 121518212427303336
1
Impulse response of
UnRate
-1
3 6 9 121518212427303336
2
Impulse response of
ISMManEmp
3 6 9 121518212427303336
2
0
1
1
-1
0
0
-2
10
-1
Impulse response of
ISMNMEmp
-1
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
CapU
Impulse response of
CPIall
Impulse response of
LabAvH
4
4
5
2
2
0
0
0
-5
-2
3 6 9 121518212427303336
-2
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 9: IRF Response to Monetary Stance (st ) shock: Real Variables Reaction
22
Impulse response of Econ Sit (EcSit)
1
0.5
0
-0.5
-1
3
6
9
12
15
18
21
24
27
30
33
36
30
33
36
30
33
36
Impulse response of Monetary Stance (s)
0.5
0
-0.5
-1
3
6
9
12
15
18
21
24
27
Impulse response of Fwd Guidance (FG)
0
-0.5
-1
-1.5
3
6
9
12
15
18
21
24
27
Figure 10: IRF Response to FGt shock: Policy Variable Reaction
0.5
Impulse response of
3mYield
0.5
Impulse response of
1yrYield
0.5
0
0
0
-0.5
-0.5
-0.5
-1
-1
3 6 9 121518212427303336
0
Impulse response of
5yrYield
-1
3 6 9 121518212427303336
0
Impulse response of
3yrYield
Impulse response of
10yrYield
3 6 9 121518212427303336
1
Impulse response of
Spr10y-2y
0.5
-0.5
-0.5
0
-1
0
-1
-0.5
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
aaaYield
Impulse response of
baaYield
Impulse response of
SprAAA-10y
0.5
1
-0.2
0
0.5
-0.4
-0.5
0
-0.6
-1
3 6 9 121518212427303336
-0.5
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 11: IRF Response to FGt shock: Yields Reaction
23
VAR and identifies monetary policy shocks under QE using heteroskedasticity (particularly that monetary policy shocks are relatively more volatile around U.S. monetary
policy announcements.) He finds that expansionary monetary policy shocks boost asset
prices but that the effects are not long-lived. A main difference is that we have tried to
isolate the effects of specific aspects of communication.
The shocks to forward guidance also affect market variables in the expected way.
The impulse responses of a selection of markets variables is presented in 12. For example, equity is estimated to respond positively to more certainty about future monetary
expansion (though imprecisely estimated). The dollar tends to depreciate with the news.
0.5
Impulse response of
Michigan
1
0
0.5
-0.5
0
Impulse response of
BBD
0.5
Impulse response of
Vix
0
-1
-0.5
3 6 9 121518212427303336
100
Impulse response of
SP500
-0.5
3 6 9 121518212427303336
40
Impulse response of
NASDAQ
3 6 9 121518212427303336
200
Impulse response of
EquityIndexWilson
20
50
100
0
0
10
0
-20
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
USDTWImajor
Impulse response of
GasPrice
Impulse response of
OilPrice
0.2
1
5
0
0.5
0
-0.2
0
-5
-0.4
3 6 9 121518212427303336
-0.5
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 12: IRF Response to FGt shock: Markets Reaction
However, the effects on real variables are much less clear cut and much noisier (figure
13). More expansionary forward guidance would, with a lag, start to push activity and
labour market variables in the expected (or hoped) direction. But the evidence of a clear
effect on real activity is difficult to gauge.
6.3
The effect of a change in Economic Situation Balance
For our final analysis of impulse responses, we turn to the effects of a shock to EcSitt . A
negative shock is equivalent to the FOMC statement talking more about economic contraction in their post-meeting statement. Figure 14 presents the shock, and the response
of the other policy variables, while figures 16 to 17 present the response of the other
variables we have analysed before.
24
40
Impulse response of
IP
0.5
Impulse response of
ISMManProd
Impulse response of
ISMNMActivity
0.5
20
0
0
0
-0.5
-20
1
-0.5
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
UnRate
Impulse response of
ISMManEmp
Impulse response of
ISMNMEmp
0.5
0.5
0.5
0
0
0
-0.5
-0.5
3 6 9 121518212427303336
0.5
-0.5
3 6 9 121518212427303336
Impulse response of
CapU
20
Impulse response of
CPIall
3 6 9 121518212427303336
Impulse response of
LabAvH
0.2
10
0
0
-0.2
0
-0.5
-10
3 6 9 121518212427303336
-0.4
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 13: IRF Response to FGt shock: Real Variables Reaction
Impulse response of Econ Sit (EcSit)
0.5
0
-0.5
-1
-1.5
3
6
9
12
15
18
21
24
27
30
33
36
30
33
36
30
33
36
Impulse response of Monetary Stance (s)
0.4
0.2
0
-0.2
3
6
9
12
15
18
21
24
27
Impulse response of Fwd Guidance (FG)
0.4
0.2
0
-0.2
3
6
9
12
15
18
21
24
27
Figure 14: IRF Response to EcSitt shock: Policy Variable Reaction
25
0.4
Impulse response of
3mYield
0.4
Impulse response of
1yrYield
0.4
0.2
0.2
0.2
0
0
0
-0.2
-0.2
3 6 9 121518212427303336
0.4
Impulse response of
5yrYield
-0.2
3 6 9 121518212427303336
0.4
Impulse response of
10yrYield
3 6 9 121518212427303336
0.2
0.2
0.2
0
0
0
-0.2
-0.2
0.4
Impulse response of
3yrYield
-0.2
Impulse response of
Spr10y-2y
-0.4
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
aaaYield
Impulse response of
baaYield
Impulse response of
SprAAA-10y
0.2
0.2
0.2
0
0
0
-0.2
-0.2
-0.2
3 6 9 121518212427303336
-0.4
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 15: IRF Response to EcSitt shock: Yields Reaction
There is almost no significant reaction of yields (figure 15), markets variables (16)
nor real variables.Some of the impulse responses seem to be intuitive, such as corporate
bond yields falling, while others seem unintuitive, such as purchasing managers’ survey
responses indicating more activity about 6 months after the statement. This is despite
being ordered first of the monetary policy variables. It seems that the FOMC shocks that
reveal the current economic situation do not affect the variables in the way that FOMC
guidance about their future policy. Perhaps this is because the markets react more to
other, more quantitative, information released by the FOMC or that they update their
views of the economy in a similar way to the FOMC in response to economic releases
such that there is little news in the FOMC view about the economy, but only news in
how the FOMC intends to react to it (captured more by F Gt ).
6.4
Analysis of the Forecast Error Variance Decomposition
In order to understand how important each of these dimensions of monetary policy and
communication is, we turn to the analysis of Forecast Error Variance Decompositions
(FEVD) from the FAVAR system. This is, like the impulse response functions, derived
from the structural VMA representation. Specficially, it looks at the variance in the h
period ahead forecast error that can be attributed to each shock. Hence, we can use
the FEVD to quantify how important different shocks are for each variable at different
horizons.
Figure 18 shows the FEVD explained by monetary shocks for a selection of rates (18a
26
0.4
Impulse response of
Michigan
0.1
Impulse response of
BBD
0.2
0.2
0
0
0
-0.1
-0.2
-0.2
2
-0.2
-0.4
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
SP500
Impulse response of
NASDAQ
Impulse response of
EquityIndexWilson
2
2
1
1
1
0
0
0
-1
-1
3 6 9 121518212427303336
4
Impulse response of
Vix
Impulse response of
USDTWImajor
-1
3 6 9 121518212427303336
0.2
Impulse response of
GasPrice
3 6 9 121518212427303336
0.1
2
0
0
0
-0.2
-0.1
-2
-0.4
3 6 9 121518212427303336
Impulse response of
OilPrice
-0.2
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 16: IRF Response to EcSitt shock: Markets Reaction
40
Impulse response of
IP
0.4
Impulse response of
ISMManProd
0.4
20
0.2
0.2
0
0
0
-20
-0.2
3 6 9 121518212427303336
0.2
Impulse response of
UnRate
-0.2
3 6 9 121518212427303336
0.4
Impulse response of
ISMManEmp
3 6 9 121518212427303336
0.4
0
0.2
0.2
-0.2
0
0
-0.4
0.4
Impulse response of
ISMNMActivity
-0.2
Impulse response of
ISMNMEmp
-0.2
3 6 9 121518212427303336
3 6 9 121518212427303336
3 6 9 121518212427303336
Impulse response of
CapU
Impulse response of
CPIall
Impulse response of
LabAvH
10
0.4
0.2
5
0.2
0
0
0
-0.2
-5
3 6 9 121518212427303336
-0.2
3 6 9 121518212427303336
3 6 9 121518212427303336
Figure 17: IRF Response to EcSitt shock: Real Variables Reaction
27
100%
FG
EcSit
Stance
FG
EcSit
Stance
90%
100%
80%
90%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
1M 6M 12M60M
1M 6M 12M60M
3mYield
1yrYield
(a)
1M 6M 12M60M
1M 6M 12M60M
3yrYield
1M 6M 12M60M
5yrYield
0%
1M 6M 12M60M
10yrYield
1M 6M 12M60M
3mYield
FEVD: Yields
1yrYield
(b)
EcSit
Stance
1M 6M 12M60M
3yrYield
1M 6M 12M60M
5yrYield
10yrYield
Relative contribution: Yields
100%
FG
1M 6M 12M60M
FG
EcSit
Stance
90%
100%
80%
90%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0%
1M 6M 12M60M
1M 6M 12M60M
Vix
SP500
(c)
1M 6M 12M60M
NASDAQ
1M 6M 12M60M
1M 6M 12M60M
1M 6M 12M60M
EquityIndexWilson
Vix
USDTWImajor
FEVD: Market Variables
(d)
SP500
EcSit
1M 6M 12M60M
NASDAQ
1M 6M 12M60M
1M 6M 12M60M
EquityIndexWilson
USDTWImajor
Relative contribution: Market Variables
100%
FG
1M 6M 12M60M
FG
EcSit
Stance
100%
90%
Stance
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
1M 6M 12M60M
CapU
1M 6M 12M60M
ISMManEmp
(e)
1M 6M 12M60M
ISMNMEmp
1M 6M 12M60M
1M 6M 12M60M
UE
0%
1M 6M 12M60M
CPI
CapU
FEVD: Real Variables
(f )
1M 6M 12M60M
ISMManEmp
1M 6M 12M60M
1M 6M 12M60M
ISMNMEmp
1M 6M 12M60M
UE
Relative contribution: Real Variables
Figure 18: Forecast Error Variance Decomposition Analysis
Notes: The column on the left shows the FEVD for each of the three monetary dimensions. The column on the right simply shows the relative contribution of a given monetary
dimension to the overall variance explained by the three dimensions of monetary policy
together.
28
CPI
and 18b), market variables (18c and 18d) and real variables (18e and 18f). These are
shown for one month (1M), six months (6M), one year (12M) and five year (60M) forecast
horizons. The rows show, respectively, the response of yields and spreads, other financial
market variables, and the response of a selection of real variables. The first column shows
the contribtion of each dimension of monetary policy as a share of the total forecast error
variance while the second column focuses only on the relative contribution of each of
the dimensions of FOMC decisions and communication to the total contribution from
monetary sources.
The contribution of all dimensions of monetary policy to the forecast error variance
of the selected variables ranges between around 65% for 10 year yields at the one month
horizon, to below 5% for some of the real economic variables at the one month horizon.
As might be expected, as we move to longer forecast horizons, the role of the monetary
dimensions tends to grow for real variables (up to around 30% for some variables) while
the role in explaining yields tends to decline. Of course, at longer horizons it is other
shocks (not studied here) which explain the variance of most variables. This is in line
with previous VAR and FAVAR studies such as Bernanke, Boivin, and Eliasz (2005).
In terms of the relative importance of the three dimensions that we study, the most
important dimension of monetary policy remains the current monetary stance accounting
for at least 50% of the total monetary contribution, and typcially 60-70%. In terms of
the novel dimensions studied in this paper, the results reinforce the earlier IRF results.
Namely, shocks to FGt seem to explain the movement of yields data, especially at longer
maturities and at shorter forecast horizons, but they explain only a small portion of the
shocks to market data and real variables. In all cases, the shocks to EcSitt explain a
smaller amount of the variability in the variables.
7
Conclusion
In this paper we empirically explore the channels through which central bank communication has effects. Moreover, we have tried to ascertain whether the effects of FOMC
communication on markets is persistent and whether there are effects on real variables.
Using tools from computational linguistics, we have measured two important characteristics of FOMC statements and found that, at least in the last 18 years in the US, the
central bank guidance on future interest rates seems to have been more important than
their communication of economic conditions. Nonetheless, neither communication has
particularly strong effects on real economic variables in our FAVAR.
A number of extensions of this paper are warranted in future work. The first is to
extend the analysis to other forms of FOMC communication; perhaps speeches and other
communications such as the FOMC meeting minutes might contain information that
29
investors learn from and that affects economic outcomes. Second, it would useful to see
if there is a time-varying role of the effects of central bank communication. In particular,
the effects of central bank communication may change when interest rates hit the zero
lower bound. Third, it could be that there are interactions between monetary stance and
communication. Perhaps the stance is only found to have a strong role because of the
communication that has gone with it. Finally, it would be useful to extend the analysis
to other countries and thereby see if communication plays a similar role. For example,
there is a longer history of forward guidance in Sweden which would be useful to analyse.
We leave these for future work.
30
References
Acosta, M. (2015): “FOMC Responses to Calls for Transparency,” Finance and Economics Discussion Series 2015-60, Board of Governors of the Federal Reserve System
(U.S.).
Apel, M., and M. Blix Grimaldi (2012): “The Information Content of Central Bank
Minutes,” Working Paper Series 261, Sveriges Riksbank (Central Bank of Sweden).
Bailey, A., and C. Schonhardt-Bailey (2008): “Does Deliberation Matter in
FOMC Monetary Policymaking? The Volcker Revolution of 1979,” Political Analysis, 16, 404–427.
Bauer, M. D., and G. D. Rudebusch (2013): “Monetary policy expectations at the
zero lower bound,” Discussion paper.
Belviso, F., and F. Milani (2006): “Structural Factor-Augmented VARs (SFAVARs)
and the Effects of Monetary Policy,” The B.E. Journal of Macroeconomics, 6(3), 1–46.
Berger, H., M. Ehrmann, and M. Fratzscher (2011): “Monetary Policy in the
Media,” Journal of Money, Credit and Banking, 43(4), 689–709.
Bernanke, B., J. Boivin, and P. S. Eliasz (2005): “Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach,” The
Quarterly Journal of Economics, 120(1), 387–422.
Blei, D., and J. Lafferty (2009): “Topic models,” in Text Mining: Classification,
Clustering, and Applications, ed. by A. Srivastava, and M. Sahami. Taylor & Francis,
London, England.
Blei, D. M., A. Y. Ng, and M. I. Jordan (2003): “Latent Dirichlet Allocation,”
Journal of Machine Learning Research, 3, 993–1022.
Blinder, A. S., M. Ehrmann, M. Fratzscher, J. D. Haan, and D.-J. Jansen
(2008): “Central Bank Communication and Monetary Policy: A Survey of Theory and
Evidence,” Journal of Economic Literature, American Economic Association, 46(4),
910–45.
Campbell, J., C. Evans, J. Fisher, and A. Justiniano (2012): “Macroeconomic
Effects of Federal Reserve Forward Guidance,” The Brookings Papers on Economic
Activity, Spring, 1–54.
Christiano, L. J., M. Eichenbaum, and C. L. Evans (1999): “Monetary policy
shocks: What have we learned and to what end?,” in Handbook of Macroeconomics,
ed. by J. B. Taylor, and M. Woodford, vol. 1 of Handbook of Macroeconomics, chap. 2,
pp. 65–148. Elsevier.
Ehrmann, M., and M. Fratzscher (2007): “Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness?,” Journal of Money, Credit
and Banking, 39(2/3), pp. 509–541.
Eusepi, S., and B. Preston (2010): “Central Bank Communication and Expectations
Stabilization,” American Economic Journal: Macroeconomics, 2(3), 235–71.
Fligstein, N., J. S. Brundage, and M. Schultz (2014): “Why the Federal Reserve Failed to See the Financial Crisis of 2008: The Role of ”Macroeconomics” as a
Sensemaking and Cultural Frame,” Mimeograph, University of California Berkeley.
31
Gürkaynak, R. S., B. Sack, and E. Swanson (2005): “Do Actions Speak Louder
Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements,” International Journal of Central Banking, 1(1).
Hansen, S., M. McMahon, and A. Prat (2014): “Transparency and Deliberation
within the FOMC: a Computational Linguistics Approach,” Discussion Papers, Centre
for Economic Policy Research (CEPR) 9994, Centre for Economic Policy Research
(CEPR).
Hayo, B., A. M. Kutan, and M. Neuenkirch (2012): “Federal Reserve Communications and Emerging Equity Markets,” Southern Economic Journal, Southern Economic
Association, 78(3), 1041–1056.
Hayo, B., and M. Neuenkirch (2010): “Do Federal Reserve communications help
predict federal funds target rate decisions?,” Journal of Macroeconomics, Elsevier,
32(4), 1014–1024.
Hendry, S. (2012): “Central Bank Communication or the Media’s Interpretation: What
Moves Markets?,” Working Papers 12-9, Bank of Canada.
Hendry, S., and A. Madeley (2010): “Text Mining and the Information Content of
Bank of Canada Communications,” Working Papers 10-31, Bank of Canada.
Hernández-Murillo, R., and H. Shell (2014): “The Rising Complexity of the
FOMC Statement,” Economic Synopses, 23.
King, R. G., Y. K. Lu, and E. S. Pastén (2008): “Managing Expectations,” Journal
of Money, Credit and Banking, 40(8), 1625–1666.
Krippner, L. (2013): “A tractable framework for zero-lower-bound Gaussian term structure models,” CAMA Working Papers 2013-49, Centre for Applied Macroeconomic
Analysis, Crawford School of Public Policy, The Australian National University.
Loughran, T., and B. McDonald (2011): “When is a Liability not a Liability?
Textual Analysis, Dictionaries, and 10-Ks,” Journal of Finance, 66, 35–65.
(2014): “Measuring Readability in Financial Disclosures,” Journal of Finance,
69, 1643–1671.
Lucca, D. O., and F. Trebbi (2009): “Measuring Central Bank Communication: An
Automated Approach with Application to FOMC Statements,” NBER Working Papers
15367, National Bureau of Economic Research, Inc.
Meyer, L. (2012): “General Discussion of “Macroeconomic Effects of Federal Reserve
Forward Guidance”,” The Brookings Papers on Economic Activity, Spring, 77.
Quinn, K. M., B. L. Monroe, M. Colaresi, M. H. Crespin, and D. R. Radev
(2010): “How to Analyze Political Attention with Minimal Assumptions and Costs,”
American Journal of Political Science, 54(1), 209–228.
Ranaldo, A., and E. Rossi (2010): “The reaction of asset markets to Swiss National
Bank communication,” Journal of International Money and Finance, Elsevier, 29(3),
486–503.
Reis, R. (2013): “Central Bank Design,” Journal of Economic Perspectives, 27(4), 17–
44.
32
Romer, D. (2012): “Discussion of “Macroeconomic Effects of Federal Reserve Forward
Guidance”,” The Brookings Papers on Economic Activity, Spring, 74.
Rosa, C., and G. Verga (2008): “The Impact of Central Bank Announcements on
Asset Prices in Real Time,” International Journal of Central Banking, International
Journal of Central Banking, 4(2), 175–217.
Schonhardt-Bailey, C. (2013): Deliberating Monetary Policy. MIT Press, Cambridge.
Stock, J. H., and M. W. Watson (2001): “Vector Autoregressions,” Journal of
Economic Perspectives, 15(4), 101–115.
Taylor, J. B. (1993): “Discretion versus policy rules in practice,” Carnegie-Rochester
Conference Series on Public Policy, 39(1), 195–214.
Tetlock, P. (2007): “Giving Content to Investor Sentiment: The Role of Media in the
Stock Market,” Journal of Finance, 62, 1139–1168.
Tetlock, P., M. Saar-Tsechansky, and S. Macskassy (2008): “More Than
Words: Quantifying Language to Measure Firms’ Fundamentals,” Journal of Finance,
63, 1437–1467.
Woodford, M. (2012): “Discussion of “Macroeconomic Effects of Federal Reserve Forward Guidance”,” The Brookings Papers on Economic Activity, Spring, 64–73.
Wright, J. H. (2012): “What does Monetary Policy do to Longterm Interest Rates at
the Zero Lower Bound?,” Economic Journal, 122(564), F447–F466.
Wu, J. C., and F. D. Xia (2014): “Measuring the Macroeconomic Impact of Monetary
Policy at the Zero Lower Bound,” NBER Working Papers 20117, National Bureau of
Economic Research, Inc.
33
A
FRED
code
USEPUINDXM
ShillerHP
HSN1F
MSPNHSUS
INDPRO
IPBUSEQ
IPDCONGD
IPDMAT
IPG3361S
IPMAN
IPMAT
IPNCONGD
TCU
MCUMFN
CAPUTLGMFDS
M1SL
M2SL
AMBSL
EXCSRESNS
MCOILWTICO
MHHNGSP
NAPMPI
NAPMBI
NAPMEI
NAPMNOI
NAPMPRI
NMFBAI
NMFBI
NMFEI
NMFNOI
NMFPI
PAYEMS
Transform
code
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
1
1
1
1
1
1
1
1
1
5
Slow-move
code
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
Long
Description
Economic Policy Uncertainty Index for United States
Case-Shiller Real House Price index
New One Family Houses Sold: United States
Median Sales Price for New Houses Sold in the United States
Industrial Production Index
Industrial Production: Business Equipment
Industrial Production: Durable Consumer Goods
Industrial Production: Durable Materials
Industrial Production: Durable Goods: Motor vehicle
Industrial Production: Manufacturing (NAICS)
Industrial Production: Materials
Industrial Production: Nondurable Consumer Goods
Capacity Utilization: Total Industry
Capacity Utilization: Manufacturing (NAICS)
Capacity Utilization: Durable manufacturing
M1 Money Stock
M2 Money Stock
St. Louis Adjusted Monetary Base
Excess Reserves of Depository Institutions
Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma
Henry Hub Natural Gas Spot Price
ISM Manufacturing: Production Index
ISM Manufacturing: Backlog of Orders Index
ISM Manufacturing: Employment Index
ISM Manufacturing: New Orders Index
ISM Manufacturing: Prices Index
ISM Non-manufacturing: Business Activity Index
ISM Non-manufacturing: Backlog of Orders Index
ISM Non-manufacturing: Employment Index
ISM Non-manufacturing: New Orders Index
ISM Non-manufacturing: Prices Index
All Employees: Total nonfarm
US Macroeconomic Data Used in XtU S
Short
title
BBD
RealHP
SoldHouses
NomHP
IP
IP-BusEq
IP-DurC
IP-DurMat
IP-DurMotor
IP-Man
IP-Mat
IP-NdurCons
CapU
CapUMan
CapU-ManDur
M1
M2
MB
ExcReserves
OilPrice
GasPrice
ISM-ManProd
ISM-ManBack
ISM-ManEmp
ISM-ManNewO
ISM-ManPrice
ISM-NMActivity
ISM-NMBack
ISM-NMEmp
ISM-NMNewO
ISM-NMPrice
Lab-EmpTot
34
Lab-EmpCons
Lab-EmpGoods
Lab-EmpSer
Lab-AvH
Unemployment rate
P-CED
PPI-all
PPI-Cons
PPI-Finished
PPI-FinishLessEnergy
CPI-all
CPI-apparel
CPI-Medical
CPI-trans
USD-Can
USD-China
USD-Jap
USD-Mex
USD-Swiss
USD-UK
USD-TWI
USD-TWImajor
RPDIpc
RPDI
TotalLoans
Michigam
SP500
SP500-PE
NASDAQ
EquityIndex-Wilson
Vix
1yrYield
10yrYield
3yrYield
3mYield
5yrYield
aaaYield
35
USCONS
USGOOD
SRVPRD
CEU0500000007
UnRate
PCEPI
PPIACO
PPIFCG
PPIFGS
PPILFE
CPIAUCSL
CPIAPPSL
CPIMEDSL
CPITRNSL
EXCAUS
EXCHUS
EXJPUS
EXMXUS
EXSZUS
EXUSUK
TWEXBMTH
TWEXMMTH
A229RX0
W875RX1
TOTALSL
UMCSENT
ShillerSandP
ShillerPE
NASDAQCOM
WILL5000INDFC
VIXCLS
GS1
GS10
GS3
GS3M
GS5
AAA
5
5
5
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
5
4
5
5
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
All Employees: Construction
All Employees: Goods-Producing Industries
All Employees: Service-Providing Industries
Average Weekly Hours of Production and Nonsupervisory Employees: Total Private
Unemployment Rate
Personal Consumption Expenditures: Chain-type Price Index
Producer Price Index for All Commodities
Producer Price Index by Commodity for Finished Consumer Goods
Producer Price Index by Commodity for Finished Goods
Producer Price Index by Commodity for Finished Goods Less Food & Energy
Consumer Price Index for All Urban Consumers: All Items
Consumer Price Index for All Urban Consumers: Apparel
Consumer Price Index for All Urban Consumers: Medical Care
Consumer Price Index for All Urban Consumers: Transportation
Canada - U.S. Foreign Exchange Rate
China - U.S. Foreign Exchange Rate
Japan - U.S. Foreign Exchange Rate
Mexico - U.S. Foreign Exchange Rate
Switzerland - U.S. Foreign Exchange Rate
U.S. - U.K. Foreign Exchange Rate
Trade Weighted U.S. Dollar Index: Broad
Trade Weighted U.S. Dollar Index: Major Currencies
Real Disposable Personal Income: Per capita
Real personal income excluding current transfer receipts
Total Consumer Credit Owned and Securitized, Outstanding
University of Michigan: Consumer Sentiment
S&P500 Equity Index
S&P500 PE ratio
NASDAQ Composite Index
Wilshire 5000 Total Market Full Cap Index
CBOE Volatility Index: VIX
1-Year Treasury Constant Maturity Rate
10-Year Treasury Constant Maturity Rate
3-Year Treasury Constant Maturity Rate
3-Month Treasury Constant Maturity Rate
5-Year Treasury Constant Maturity Rate
Moody’s Seasoned Aaa Corporate Bond Yield
baaYield
SprAAA-10y
SprBAA-10y
Spr10y-2y
Spr10y-3m
Spr10y-ffr
Spr3m-ffr
36
BAA
AAA10YM
BAA10YM
T10Y2YM
T10Y3MM
T10YFFM
TB3SMFFM
1
1
1
1
1
1
1
0
0
0
0
0
0
0
Moody’s Seasoned Baa Corporate Bond Yield
Moody’s Seasoned Aaa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity
Moody’s Seasoned Baa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity
10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity
10-Year Treasury Constant Maturity Minus 3-Month Treasury Constant Maturity
10-Year Treasury Constant Maturity Minus Federal Funds Rate
3-Month Treasury Bill Minus Federal Funds Rate
Table A.2: Word lists and frequency across FOMC statements in sample
Expansion words
Stemmed Token Frequency
improv
55
foster
52
increas
42
expand
38
rise
27
higher
14
risen
10
gain
9
strong
5
acceler
1
faster
1
strength
1
Contraction words
Stemmed Token Frequency
moder
82
slow
35
low
33
weak
27
subdu
20
lower
20
fall
13
slower
5
weaker
3
decreas
3
weaken
2
contract
2
soften
2
deceler
1
cool
1
37
Ambiguity Words in Sample
Stemmed Token Frequency
condit
91
anticip
71
believ
20
risk
14
may
14
appear
11
conting
9
suggest
9
seem
7
somewhat
4
uncertainti
4
uncertain
3
possibl
2
destabil
2
volatil
1
tent
1
unusu
1
might
1
alter
1